FC-DenseNet-Tensorflow

This is a re-implementation of the 100 layer tiramisu, technically a fully convolutional DenseNet, in TensorFlow (Tiramisu). The aim of the repository is to break down the working modules of the network, as presented in the paper, for ease of understanding. To facilitate this, the network is defined in a class, with functions for each block in the network. This promotes a modular view, and an understanding of what each component does, individually.
I tried to make the the model code more readable, and this is the main aim of the this repository.

Network Architecture

Submodules

The "submodules" that build up the Tiramisu are explained here.
Note: The graphics are just a redrawing of the ones from the original paper.

The Conv Layer:

The "conv layer" is the most atomic unit of the FC-DenseNet, it is the building block of all other modules. The following image shows the conv layer:

As can be seen, each "convolutional" layer is actually a 4 step procedure of batch normalization -> Relu -> 2D-Convolution -> Dropout.

The Dense Block

The dense block is a sequence of convolutions followed by concatenations. The output of a conv layer is concated depth wise with its input, this forms the input to the next layer, and is repeated for all layers in a dense block. For the final output i.e., the output of the Dense Block, all the outputs of each conv layer in the block are concated, as shown: